Density Estimation

Description

Estimates the density of a vector of observations by a Poisson regression fit to histogram counts.

Usage

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  denspr(x, n.interval = NULL, df = 5, knots.mode = TRUE, 
      type.nclass = c("wand", "scott", "FD"), addx=FALSE)

Arguments

x

a numeric vector containing the observations for which the density should be estimated.

n.interval

an integer specifying the number of cells for the histogram. If NULL, n.interval is estimated by the method specified by type.nclass.

df

integer specifying the degrees of freedom of the natural cubic spline used in the Poisson regression fit.

knots.mode

if TRUE the df - 1 knots are centered around the mode and not the median of the density, where the mode is estimated by the midpoint of the cell of the histogram that contains the largest number of observations. If FALSE, the default knots are used in the function ns. Thus, if FALSE the basis matrix will be generated by ns(x, df = 5).

type.nclass

character string specifying the procedure used to compute the number of cells of the histogram. Ignored if n.interval is specified. By default, the method of Wand (1994) with level = 1 (see the help page of dpih in the package KernSmooth) is used. For the other choices, see nclass.scott.

addx

should x be added to the output? Necessary when the estimated density should be plotted by plot(out) or lines(out), where out is the output of denspr.

Value

An object of class denspr consisting of

y

a numeric vector of the same length as x containing the estimated density for each of the observations

center

a numeric vector specifying the midpoints of the cells of the histogram

counts

a numeric vector of the same length as center composed of the number of observations of the corresponding cells

x.mode

the estimated mode

ns.out

the output of ns

type

the method used to estimate the numbers of cells

x

the input vector x if addx = TRUE; otherwise, NULL.

Author(s)

Holger Schwender,holger.schw@gmx.de

References

Efron, B., and Tibshirani, R. (1996). Using specially designed exponential families for density estimation. Annals of Statistics, 24, 2431–2461.

Wand, M.P. (1997). Data-based choice of histogram bin width. American Statistician, 51, 59–64.

See Also

cat.ebam

Examples

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## Not run: 
# Generating some random data.
x <- rnorm(10000)
out <- denspr(x, addx=TRUE)
plot(out)

# Or for an asymmetric density.
x <- rchisq(10000, 2)
out <- denspr(x, df=3, addx=TRUE)
plot(out)

## End(Not run)

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